CN103605110A - Indoor passive target positioning method based on received signal strength - Google Patents

Indoor passive target positioning method based on received signal strength Download PDF

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CN103605110A
CN103605110A CN201310642652.0A CN201310642652A CN103605110A CN 103605110 A CN103605110 A CN 103605110A CN 201310642652 A CN201310642652 A CN 201310642652A CN 103605110 A CN103605110 A CN 103605110A
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amp
step
link
target
shadow fading
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CN201310642652.0A
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CN103605110B (en
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王正欢
刘珩
许胜新
卜祥元
安建平
湛沙
范远璋
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北京理工大学
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/12Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves by co-ordinating position lines of different shape, e.g. hyperbolic, circular, elliptical, radial
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/14Determining absolute distances from a plurality of spaced points of known location

Abstract

The invention discloses an indoor passive target positioning method based on received signal strength, and belongs to the technical field of target detection and tracking in a wireless network. Considering the situation that a link is blocked by a target, the method is mainly applied to detection of links with a shadow fading phenomenon before positioning. The method comprises the steps of node arrangement, measurement of change values of the received signal strength of all the links when a monitored area is free of targets and a target enters the monitored area, time domain detection for excluding some mutant links without the shadow fading phenomenon, space domain detection for excluding the links with the shadow fading phenomenon far away from the actual position of the target, and estimation of the position of the target according to the links with the shadow fading phenomenon. The indoor passive target positioning method based on received signal strength is easy and flexible to implement; moreover, in combination with a time domain and space domain detection method, the influence of the links without the shadow fading phenomenon is effectively eliminated, the attenuation effect brought by the target is highlighted more accurately, so that indoor target positioning is more accurate, and better dynamic tracking is realized.

Description

Indoor passive object localization method based on received signal strength

Technical field

The present invention relates to a kind of indoor passive object localization method, especially a kind of placement technology based on received signal strength (Received Signal Strength, RSS), belongs to target detection and tracking technique field in wireless network.

Background technology

The technology that positions tracking for the human body target that does not carry any electronic tag becomes the focus of concern in recent years.

While being in wireless link due to people, the decay of signal can be caused, therefore the track and localization of realize target can be carried out by measuring the variation of RSS.Because existing most of wireless device, as WiFi or WSN (Wireless Sensor Network) node etc., can provide RSS value, therefore in application, do not need extra hardware condition, just can realize low-cost location.

Yet, in the location technology changing based on RSS, the variation of RSS may be from target the attenuation effect causing of blocking to sighting distance in link (Line of Sight, LOS) path.But in indoor environment, the variation of RSS is probably also that the transient change due to multipath undesired signals such as indoor each reflection, scatterings causes.These undesired signals both cannot be measured, and also cannot predict its various variable condition, but very large on RSS impact.These characteristics make indoor positioning technology be faced with at present very large challenge.

Indoor passive target localization based on RSS mainly adopts " fingerprint matching " method at present.The method needs huge received signal strength database of model.When people enters monitored area, by each signal strength values of Matched measurement and the value in database, position.Intuitively, the method underaction, is not suitable for urgent occasion, and building database need to expend very large workload.

When people enters the network that radio node forms, the propagation of blocking signal due to people can cause the decay of signal.When being on the more intense reflection of the los path of link or signal or scattering path as people, this all can cause link RSS to have obvious variation.Outdoor passive Technology for Target Location based on RSS is exactly directly according to the variation of RSS, to position at present, respond well.Less mainly due to outdoor environment reflection or scattered signal etc., the variation of RSS is mainly derived from the signal attenuation that target occlusion los path causes.In Technology for Target Location, only have in theory those because los path blocks, to cause the vicissitudinous link of RSS to provide Useful Information to target localization.But in indoor environment, reflection, scattered signal etc. are larger.When people enters the network of radio node formation, not only can cause decay to some los path signal, and can affect relevant reflection, scattered signal.If now only consider RSS to change, further do not judge whether this variation exists error very greatly because shadow fading causes making indoor Technology for Target Location.

Summary of the invention

The present invention is directed to multipath in indoor wireless location technology and disturb greatly, the complicated dumb or accurate not problem of measuring method, has proposed a kind of based on the new measuring method of the passive indoor positioning of RSS.The method is considered when link is by the situation of target occlusion, is mainly used in the front link to generation shadow fading in location and detects.From time domain characteristic, consider, because people needs the regular hour through link, so the attenuation change of RSS also can continue for some time.We just can get rid of some RSS by the detection in time domain like this has sudden change but not due to people, blocks the link of the accidental variation that LOS path causes.From space domain characteristic, consider, its point of crossing of link that shadow fading occurs presents in the gathering that approaches people's actual position place.The present invention proposes the spatial filter method based on this characteristic.

Based on the indoor passive object localization method of RSS, specifically comprise the following steps:

Step 1: n radio node is deployed in to a room area, and all nodes all in one plane;

The coordinate of each node is known quantity, and the coordinate of establishing i node is (x i, y i), i ∈ [1,2 .., n].Each node can record the RSS value of the wireless signal that other nodes send out, and these radio nodes form wireless links

Step 2: when driftlessness, measure the RSS value r of each link l, l ∈ [1,2 ..., L];

Step 3: at t constantly, target enters monitored area, and position coordinates is X t=(x t, y t), record the now RSS value r of each link l,t, l ∈ [1,2 ..., L], can obtain thus the changing value Δ r of RSS l,t=r l,t-r l, l ∈ [1,2 ..., L];

Δ r l,tbeing that concrete reason may be due to blocking of target, to cause the shadow fading of LOS due to the existence of target and kinetic, may be also to disturb due to various multipaths that target causes.

Target is equivalent to the cylinder model that radius is R.Work as d l,tduring >R, mean that target do not block link l, the LOS path of link l is not subject to the impact of target, and the variation of the RSS now recording is that the motion due to target causes the variation of this link multipath to cause.Wherein, d l,tfor the vertical range of the barycenter from target to link l, concrete calculating formula is:

d l , t = | | ( x t - x i , y t - y i ) ⊗ ( x t - x j , y t - y j ) | | ( x i - x j ) 2 + ( y i - y j ) 2 ,

In formula, (x i, y i) and (x j, y j) be two node coordinates that form link l, for cross product computing, ‖ ‖ is norm.

Work as d l,tduring≤R, the variation of the RSS recording is due to blocking of target, to cause the shadow fading of LOS.The measurement model of link l is summarized as:

Δr l , t = f l ( X t ) + n l , t = A max ( 1 - d l , t R ) + n l , t ,

In formula, shadow fading function a maxthe shadow fading value causing just on link time for target barycenter, n l,tbe to measure noise, obedience zero-mean, variance are gaussian distribution.

Step 4: time domain detects shadow fading link, by Δ r l,tthe all links that meet given Time-Domain Detection Method decision rule are designated as shadow fading link set L t, get rid of the link that some RSS have the accidental variation that suddenlys change but cause not due to target occlusion LOS path;

As preferably, directly use moving average method to detect, the decision rule that link blocks is:

1 2 w + 1 Σ t ′ = t - w t + w Δ r t ′ , l > γ

Wherein, 2w+1 is window length, { Δ r t', l: t-w≤t'≤t+w} is the changing value of the RSS of the link l that records, and γ is the thresholding factor of setting.

Can obtain so detecting with time domain approach the shadow fading link set obtaining

Step 5: to L tcarry out spatial domain shadow fading link detecting, get rid of some from target actual position the shadow fading link away from, obtain new shadow fading link subset and close L s;

Calculate L tin the intersection point of every two links, obtain all intersection point set, be designated as: P={ (u m, v m): m ∈ [1,2 .., | P|] }, in formula, (u m, v m) be the coordinate of m intersection point, || represent to ask the computing of set number.From spatial domain, LOS path is closer to each other by the intersection point forming between the link of target occlusion, and the actual position of close target.Therefore can from P, find out a maximum subset P s:

P S = { ( u i , v i ) : ( u i - u - ) 2 + ( v i - v - ) 2 < R th , &ForAll; i &Element; { 1,2 , &CenterDot; &CenterDot; &CenterDot; , | P S | } } ,

Wherein, the coordinate of subset barycenter, R thit is threshold value.Those are not at subset P sintersection point be considered to wild value.

As preferably, we realize subset search with clustering algorithm:

Definition (C 1, k, C 2, k) be k (k ∈ [1,2 ..., K]) center-of-mass coordinate of individual class, set B=b (i): i ∈ [1,2 .., | P|] be the tag set of classification results, represent the class under each intersection point in P.

Step 5.1, the number of initialization class, even K=1;

Step 5.2, the barycenter (C to each class 1, k, C 2, k) carry out initialization;

Step 5.3, For i=1:|P|, b ( i ) &LeftArrow; arg min k &Element; [ 1,2 , &CenterDot; &CenterDot; &CenterDot; , K ] ( u i - C 1 , k ) 2 + ( v i - C 2 , k ) 2 ;

Step 5.4, For k=1:K, (C 1, k, C 2, k) ← set { (u i, v i): the mean value of all elements in b (i)=k};

Step 5.5, when meeting stop condition ( u i - u - ) 2 + ( v i - v - ) 2 < R th , &ForAll; i &Element; { 1,2 , &CenterDot; &CenterDot; &CenterDot; , | P S | } Time, return to P s; If do not meet, K=K+1, gets back to step 5.2.

When more than one of the subset searching, select the subset (class) with least mean-square error.

As long as an intersection point in subset belongs to certain link, this link is just considered to shadow fading link so, obtains new shadow fading link set, is designated as: L S = { l : l &Element; L T , &Exists; i &Element; ( P S &cap; l ) } .

Step 6: according to shadow fading link set L scalculate the estimation of target location

As preferably, select the algorithm of particle filter to realize, specific implementation algorithm is as follows:

Step 6.1, t at any time, initialization t=0, produces Q particle sample value according to the probability distribution η (it is 0 that η chooses an average conventionally, the Gaussian distribution that variance is very large) setting, and is designated as subscript q represents particle index, q=1, and 2..., Q, subscript t is time index, now t=0, therefore also can be designated as

Step 6.2, the importance weight of setting each particle is and be normalized obtain, w ~ 0 ( q ) = 1 Q ;

Step 6.3, according to produce t particle constantly, upgrade weights that q particle is about the shadow fading function of link l;

Step 6.4, normalization weights &Sigma; q = 1 Q w t ( q ) = 1 , If ( &Sigma; q = 1 N ( w t ( q ) ) 2 ) - 1 < N th , N thfor the threshold value of setting, so right { X t ( q ) , w t ( q ) } q = 1 Q Resample, return to step 6.3, if ( &Sigma; q = 1 N ( w t ( q ) ) 2 ) - 1 &GreaterEqual; N th , Execution step 6.5;

Step 6.5, obtains the t estimation of target location constantly

Step 6.6, t=t+1 constantly, returns to step 6.3.

Contrast prior art, beneficial effect of the present invention is:

The indoor object localization method based on RSS that the present invention proposes, realize simple and flexible, and combine the method for time domain detection and spatial filter shadow fading link, effectively got rid of the impact of some non-shadow fading links, more accurately given prominence to the attenuation effect causing due to target occlusion, thereby make indoor target localization more accurate, realize more excellent dynamic tracking.

Accompanying drawing explanation

Fig. 1 is the indoor passive object localization method realization flow figure based on received signal strength;

Fig. 2 is the distribution of experiment node;

Fig. 3 is the explanation that people passes link l process;

Fig. 4 is the RSS value observing when people passes link;

Fig. 5 is that the shadow fading link and each link intersection point that by time domain approach, detect distribute;

Fig. 6 is the embodiment mono-of people during along square orbiting motion;

Fig. 7 is the embodiment bis-of people during along negative pulse orbiting motion.

Embodiment

Below in conjunction with drawings and Examples, the present invention is described in detail, also technical matters and beneficial effect that technical solution of the present invention solves have been narrated simultaneously, it is pointed out that described embodiment is only intended to be convenient to the understanding of the present invention, and it is not played to any restriction effect.

Fig. 1 is the indoor passive object localization method realization flow figure based on received signal strength, specifically comprises following step:

Step 1: n radio node is deployed in to a room area, and all in one plane, each node can record the RSS value of the wireless signal that other nodes send out to all nodes;

As shown in Figure 2, all nodes are in same plane, and the coordinate of each node is known, are (x in the distribution of experiment node i, y i), i ∈ [1,2 .., n].

Experiment is carried out in a general office, and middle a plurality of experiment tablees of placing do not have picture in the drawings, and this multipath that in fact can increase between each node disturbs.With 14 TI2530 transmitting and receiving modules, form 14 nodes, surround 5 * 4=20m 2scope, node roughly remains on a height, by collecting the RSS data of these 14 nodes, carrys out localizing objects.

At any time, a node broadcasts signal, another node receives and measures RSS value.The interval that RSS measured value upgrades is 0.023 second.These radio nodes form wireless links, each node can record the RSS value of the wireless signal that other nodes send out;

Step 2: when driftlessness, measure the RSS value r of each link l, l ∈ [1,2 ..., L];

Step 3: at t constantly, target enters monitored area, and position coordinates is X t=(x t, y t), record the now RSS value r of each link l,t, l ∈ [1,2 ..., L], can obtain thus the changing value Δ r of RSS l,t=r l,t-r l, l ∈ [1,2 ..., L];

Δ r l,tbeing that concrete reason may be due to blocking of target, to cause the shadow fading of LOS due to people's existence and kinetic, may be also to disturb due to various multipaths that people causes.

Fig. 3 is the explanation that people passes link l process.Target is equivalent to the cylinder model that radius is R.Work as d l,tduring >R, mean that target do not block link l, the LOS path of link l is not subject to the impact of target, and the variation of the RSS now recording is that the motion due to target causes the variation of this link multipath to cause.Wherein, d l,tfor the vertical range of the barycenter from target to link l, concrete calculating formula is:

d l , t = | | ( x t - x i , y t - y i ) &CircleTimes; ( x t - x j , y t - y j ) | | ( x i - x j ) 2 + ( y i - y j ) 2 ,

In formula, (x i, y i) and (x j, y j) be two node coordinates that form link l, for cross product computing, ‖ ‖ is norm.

Work as d l,tduring≤R, the variation of the RSS recording is due to blocking of target, to cause the shadow fading of LOS.The measurement model of link l is summarized as:

&Delta;r l , t = f l ( X t ) + n l , t = A max ( 1 - d l , t R ) + n l , t ,

In formula, shadow fading function a maxthe shadow fading value causing just on link time for target barycenter, n l,tbe to measure noise, obedience zero-mean, variance are gaussian distribution.

Step 4: time domain detects shadow fading link, by Δ r l,tin meet given Time-Domain Detection Method decision rule all links be designated as shadow fading link set L t, getting rid of some RSS has sudden change but blocks LOS path and the link of the accidental variation that causes not due to people;

Fig. 4 is the RSS value observing when people passes link.Because people needs the regular hour through link, so the attenuation change of RSS also can continue for some time.In Fig. 4, because people is through link, caused the attenuation change of RSS value between sampled point 250 to 350.So we can detect to get rid of by time domain the non-shadow fading link of some sudden changes.

As preferably, directly use moving average method to detect, the decision rule that link blocks is:

1 2 w + 1 &Sigma; t &prime; = t - w t + w &Delta; r t &prime; , l > &gamma;

Wherein, 2w+1 is window length, { Δ r t', l: t-w≤t'≤t+w} is the changing value of the RSS of the link l that records, and γ is the thresholding factor of setting.

Can obtain so detecting with time domain approach the shadow fading link set obtaining

Step 5: to L tcarry out spatial domain shadow fading link detecting, get rid of some from target actual position the shadow fading link away from, obtain new shadow fading link subset L s;

Calculate L tin the intersection point of every two links, obtain all intersection point set, be designated as: P={ (u m, v m): m ∈ [1,2 .., | P|] }, in formula, (u m, v m) be the coordinate of m intersection point, || represent to ask the computing of set number.Fig. 5 is that the shadow fading link and each link intersection point that by time domain approach, detect distribute, and from spatial domain, LOS path is closer to each other by the intersection point forming between the link of target occlusion, and the actual position of close target, as the intersection point in circle in Fig. 5.Therefore can from P, find out a maximum subset P s:

P S = { ( u i , v i ) : ( u i - u - ) 2 + ( v i - v - ) 2 < R th , &ForAll; i &Element; { 1,2 , &CenterDot; &CenterDot; &CenterDot; , | P S | } }

Wherein, the coordinate of subset barycenter, R thit is threshold value.Those are not at subset P sintersection point be considered to wild value, as the intersection point 1,2,3 in Fig. 5.

As preferably, we realize subset search with clustering algorithm:

Definition (C 1, k, C 2, k) be k (k ∈ [1,2 ..., K]) center-of-mass coordinate of individual class, set B=b (i): i ∈ [1,2 .., | P|] be the tag set of classification results, represent the class under each intersection point in P.

Step 5.1, the number of initialization class, even K=1;

Step 5.2, the barycenter (C to each class 1, k, C 2, k) carry out initialization;

Step 5.5, when meeting stop condition ( u i - u - ) 2 + ( v i - v - ) 2 < R th , &ForAll; i &Element; { 1,2 , &CenterDot; &CenterDot; &CenterDot; , | P S | } Time, return to P s; If do not meet, K=K+1, gets back to step 5.2.

When more than one of the subset searching, select the subset (class) with least mean-square error.

As long as an intersection point in subset belongs to certain link, this link is just considered to shadow fading link so.Obtain new shadow fading link set, be designated as L S = { l : l &Element; L T , &Exists; i &Element; ( P S &cap; l ) } .

Step 6: according to shadow fading link set L sobtain the estimation of target location

Obtain shadow fading link subset L safter, according to the situation of the link of shadow fading in wireless network, can estimate to obtain the position of target.As preferably, select the algorithm of particle filter to realize, specific implementation algorithm is as follows:

Step 6.1, t at any time, initialization t=0, produces Q particle sample value according to the probability distribution η (it is 0 that η chooses an average conventionally, the Gaussian distribution that variance is very large) setting, and is designated as subscript q represents particle index, q=1, and 2..., Q, subscript t is time index, now t=0, therefore also can be designated as

Step 6.2, the importance weight of setting each particle is and be normalized obtain, w ~ 0 ( q ) = 1 Q ;

Step 6.3, according to produce t particle constantly, upgrade weights that q particle is about the shadow fading function of link l;

Step 6.4, normalization weights &Sigma; q = 1 Q w t ( q ) = 1 , If ( &Sigma; q = 1 N ( w t ( q ) ) 2 ) - 1 < N th , N thfor the threshold value of setting, so right { X t ( q ) , w t ( q ) } q = 1 Q Resample, return to step 6.3, if ( &Sigma; q = 1 N ( w t ( q ) ) 2 ) - 1 &GreaterEqual; N th , Execution step 6.5;

Step 6.5, obtains the t estimation of target location constantly

Step 6.6, t=t+1 constantly, returns to step 6.3.

Each link signal in each moment network is all carried out to step 1 to the processing of step 6, can realize the motion conditions that target is located more accurately and observed in real time monitored area internal object.

Experiment parameter index is as shown in table 1.

Table 1

R A max σ l σ ε γ R th Q N th 0.3m 8dB 1dB 0.1m 3dB 0.5m 100 2/3

For the accuracy of analyzing and positioning better, target is moved with normal speed along projected path.Considered in an embodiment two kinds of movement locus---square and negative pulse.

Fig. 6 is the embodiment mono-of people during along square orbiting motion.Fig. 7 is the embodiment bis-of people during along negative pulse orbiting motion.As shown in the figure, in these two embodiment, the track of estimation conforms to the real track of target very much, and the tracking error of using above-mentioned measuring method to obtain is approximately 0.3m, and this meets the accuracy requirement of indoor positioning.

Claims (8)

1. the indoor passive object localization method based on received signal strength, is characterized in that, specifically comprises the following steps:
Step 1: n radio node is deployed in to a room area, and all in one plane, each node can record the RSS value of the wireless signal that other nodes send out to all nodes, forms wireless links;
Step 2: when driftlessness, measure the RSS value r of each link l, l ∈ [1,2 ..., L];
Step 3: at t constantly, target enters monitored area, records the now RSS value r of each link l,t, can obtain thus the changing value Δ r of RSS l,t=r l,t-r l;
Step 4: time domain detects shadow fading link, by Δ r l,tthe all links that meet given Time-Domain Detection Method decision rule are designated as shadow fading link set L t, get rid of the link that some RSS have the accidental variation that suddenlys change but cause not due to target occlusion LOS path;
Step 5: to L tcarry out spatial domain shadow fading link detecting, get rid of some from target actual position the shadow fading link away from, obtain new shadow fading link set L s;
Step 6: according to shadow fading link set L sobtain the estimation of target location
2. a kind of indoor passive object localization method based on received signal strength according to claim 1, is characterized in that, in step 3, target is equivalent to the cylinder model that radius is R, works as d l,tduring >R, the variation of the RSS recording is that the motion due to target causes the variation of this link multipath to cause, wherein, and d l,tfor the vertical range of the barycenter from target to link l, concrete calculating formula is:
d l , t = | | ( x t - x i , y t - y i ) &CircleTimes; ( x t - x j , y t - y j ) | | ( x i - x j ) 2 + ( y i - y j ) 2 ,
In formula, (x i, y i) and (x j, y j) be two node coordinates that form link l, for cross product computing, ‖ ‖ is norm.
Work as d l,tduring≤R, the variation of the RSS recording is due to blocking of target, to cause the shadow fading of LOS; The measurement model of link l is summarized as:
&Delta;r l , t = f l ( X t ) + n l , t = A max ( 1 - d l , t R ) + n l , t ,
In formula, shadow fading function a maxthe shadow fading value causing just on link time for target barycenter, n l,tbe to measure noise, obedience zero-mean, variance are gaussian distribution.
3. a kind of indoor passive object localization method based on received signal strength according to claim 1, is characterized in that, the Time-Domain Detection Method in step 4 is moving average method.
4. according to a kind of indoor passive object localization method based on received signal strength described in claim 1 or 3, it is characterized in that, in step 4, by the decision rule that moving average method detection link blocks, be:
1 2 w + 1 &Sigma; t &prime; = t - w t + w &Delta; r t &prime; , l > &gamma; ,
Wherein, 2w+1 is window length, { Δ r t', l: t-w≤t'≤t+w} is the changing value of the RSS of the link l that records, and γ is the thresholding factor of setting;
After step 4, can obtain detecting with time domain approach the shadow fading link set obtaining:
5. a kind of indoor passive object localization method based on received signal strength according to claim 1, is characterized in that, in step 5, the method for getting rid of some shadow fading links away from from target actual position is: calculate L tin the intersection point of every two links, obtain all intersection point set, be designated as:
P={(u m,v m):m∈[1,2,..,|P|]},
In formula, (u m, v m) be the coordinate of m intersection point, || represent to ask the computing of set number;
In intersection point set P, according to certain searching method, find out a maximum subset P who meets following decision rule s:
P S = { ( u i , v i ) : ( u i - u - ) 2 + ( v i - v - ) 2 < R th , &ForAll; i &Element; { 1,2 , &CenterDot; &CenterDot; &CenterDot; , | P S | } } ,
Wherein, the coordinate of subset barycenter, R thit is threshold value; Those are not at subset P sintersection point be considered to wild value;
As long as an intersection point in subset belongs to certain link, this link is just considered to shadow fading link so, obtains new shadow fading link set, is designated as: L S = { l : l &Element; L T , &Exists; i &Element; ( P S &cap; l ) } .
6. a kind of indoor passive object localization method based on received signal strength according to claim 5, is characterized in that, in step 5, described searching method is clustering algorithm, definition (C 1, k, C 2, k) be k (k ∈ [1,2 ..., K]) center-of-mass coordinate of individual class, set B=b (i): i ∈ [1,2 .., | P|] be the tag set of classification results, and represent the class under each intersection point in intersection point set P, specifically comprise the steps:
Step 5.1, the number of initialization class, even K=1;
Step 5.2, the barycenter (C to each class 1, k, C 2, k) carry out initialization;
Step 5.5, when meeting stop condition ( u i - u - ) 2 + ( v i - v - ) 2 < R th , &ForAll; i &Element; { 1,2 , &CenterDot; &CenterDot; &CenterDot; , | P S | } Time, return to P s; If do not meet, K=K+1, gets back to step 5.2.
7. a kind of indoor passive object localization method based on received signal strength according to claim 6, is characterized in that, in step 5, when more than one of the subset searching according to described searching method, selects the subset with least mean-square error.
8. a kind of indoor passive object localization method based on received signal strength according to claim 1, is characterized in that, in step 6, adopts particle filter algorithm to realize by shadow fading link set L sobtain the estimation of target location specifically comprise the steps:
Step 6.1, t at any time, initialization t=0, the probability distribution η according to setting, produces Q particle sample value, is designated as wherein, subscript q represents particle index, q=1, and 2..., Q, subscript t is time index;
Step 6.2, the importance weight of setting each particle is and be normalized
Step 6.3, according to produce t particle constantly, upgrade weights wherein, that q particle is about the shadow fading function of link l;
Step 6.4, normalization weights &Sigma; q = 1 Q w t ( q ) = 1 , If ( &Sigma; q = 1 N ( w t ( q ) ) 2 ) - 1 < N th , N thfor the threshold value of setting, so right { X t ( q ) , w t ( q ) } q = 1 Q Resample, return to step 6.3, if ( &Sigma; q = 1 N ( w t ( q ) ) 2 ) - 1 &GreaterEqual; N th , Execution step 6.5;
Step 6.5, obtains the t estimation of target location constantly
Step 6.6, t=t+1 constantly, returns to step 6.3.
CN201310642652.0A 2013-12-03 2013-12-03 Based on the indoor passive object localization method of received signal strength CN103605110B (en)

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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104093202A (en) * 2014-07-02 2014-10-08 南京信息工程大学 Environment-adaptive device-free target positioning method
CN104837199A (en) * 2015-05-26 2015-08-12 北京理工大学 Shadow fading-based wireless detection network node positioning method
CN105116375A (en) * 2015-07-16 2015-12-02 北京理工大学 Robust passive passiveness target positioning method based on multiple frequency points
CN105163327A (en) * 2014-05-26 2015-12-16 普天信息技术有限公司 Method for network program simulation of wireless communication system
WO2016082091A1 (en) * 2014-11-25 2016-06-02 华为技术有限公司 Orientation method, device and system
CN105891814A (en) * 2016-01-18 2016-08-24 中国人民解放军空军预警学院黄陂士官学校 Range-only radar networking single-target clustering positioning method
CN106255059A (en) * 2016-07-27 2016-12-21 南京师范大学 A kind of localization method without device target based on geometric ways
CN106792560A (en) * 2016-12-30 2017-05-31 北京理工大学 Target identification method based on wireless reception of signals intensity
CN106793076A (en) * 2016-12-30 2017-05-31 北京理工大学 What a kind of shadow fading was aided in exempts from Portable device localization method
CN107064872A (en) * 2017-02-14 2017-08-18 天津大学 A kind of passive type indoor orientation method and system based on intensity variation
CN107064865A (en) * 2017-04-07 2017-08-18 杭州电子科技大学 The passive co-located method of polar coordinates Dynamic Programming clustered based on depth
CN107076828A (en) * 2014-09-29 2017-08-18 意大利电信股份公司 Localization method and system for cordless communication network

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN202374456U (en) * 2011-12-06 2012-08-08 北京理工大学 Security monitoring system based on wireless radio frequency node network detection
EP2584372A1 (en) * 2011-10-17 2013-04-24 Commissariat à l'Énergie Atomique et aux Énergies Alternatives RSS based positioning method with limited sensitivity receiver

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2584372A1 (en) * 2011-10-17 2013-04-24 Commissariat à l'Énergie Atomique et aux Énergies Alternatives RSS based positioning method with limited sensitivity receiver
CN202374456U (en) * 2011-12-06 2012-08-08 北京理工大学 Security monitoring system based on wireless radio frequency node network detection

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
刘珩等: "基于传感器网络的无线层析成像方法", 《北京理工大学学报》 *

Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN104093202A (en) * 2014-07-02 2014-10-08 南京信息工程大学 Environment-adaptive device-free target positioning method
CN107076828A (en) * 2014-09-29 2017-08-18 意大利电信股份公司 Localization method and system for cordless communication network
WO2016082091A1 (en) * 2014-11-25 2016-06-02 华为技术有限公司 Orientation method, device and system
US10412549B2 (en) 2014-11-25 2019-09-10 Huawei Technologies Co., Ltd. Orientation method, device, and system
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